Coal-Rock Character Recognition in Fully Mechanized Caving Faces Based on Acoustic Pressure Data Time Domain Analysis

Abstract:

For the technical problems of coal and rock character recognition in fully mechanized caving faces. A method on characterization and recognition of coal and rock traits were discussed based on the time domain indexes of acoustic pressure data according to the differences of physics and mechanical parameters of coal and rock, and the differences of acoustic pressure data when coal and rock falling impact the rear beam of the sublevel caving hydraulic support. Firstly, the top coal caving experiments were carried out with mining portable vibration recorder developed by China University of Mining and Technology (Beijing) in fully mechanized caving faces in the underground mines, and the acoustic pressure data in quantity were acquired; Then, signal preprocessing were carried on to remove trend items for the selected acoustic pressure data; Finally, the acoustic pressure dates were analyzed in time domain and the time domain features were acquired. Comparison found, peak to peak, variance and kurtosis index are sensitive to the working conditions and the variance with a higher recognition rate. Accordingly proposed an analytical method that based on time-domain features of acoustic pressure date which used variance as recognition indicator, providing technical support for improving the caving automation and intelligent in the fully mechanized caving face.

• Periodical: Applied Mechanics and Materials (Volumes 789-790)
• Pages: 566-570
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.789-790.566
• Online since: September 2015
• Authors: Guang Hui Xue*, Er Meng Liu, Xin Ying Zhao, Bao Hua Hu, Wei Jian Ding
• Keywords: acoustic pressure data, Coal Character Recognition, Fully Mechanized Caving Faces, Rock Character Recognition, Time Domain Feature, top caving technology

References

[1] Tian Zi-jian, Peng xia, Su Bo. Research of Coal-Rock Interface Identification based on Machine Vision [J]. Industry and Mine Automation, 2013, 39(5): 49-52.

[2] Liu qiang. Method of Discerning Coal Rock Interface based on Vector Quantity Supporting Machine [J]. Applied Technology, 2007, (8): 90-91.

[3] Zhang Yan-li, Zhang Shou-xiang. Analysis of Coal and Gangue Acoustic Signals based on Hilbert-Huang Transformation [J]. Journal of China Coal Society, 2010, 35(1): 165-168.

[4] Liu wei, Hua zhen, Wang Ru-lin. Vibrational Feature Analysis for Coal Gangue Caving Based on Information Entropy of Hilbert Spectrum [J]. China Safety Science Journal , 2011, 21 (4): 32-37.

[5] Ma rui, Wang Zeng-cai, Wang Bao-ping. Coal-Rock Interface Recognition Based on Wavelet Packet Transform of Acoustic Signal [J]. Coal Mine Machinery 2010, 31(5): 44-46.

[6] Yang Jian-jian. The Development of Intrinsically Safe Vibration Sensors for Mining [J], Coal Science and Technology, 2004, 41(2): 71-74.

[7] Xue Guang-hui. Development of Mine Portable Digital Recorder [J], Coal Science and Technology, 2004, 32(5): 52-54.